





import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Any:
		__lowercase											=     [2, 2, 6, 2] if "tiny" in model_name else [2, 2, 18, 2]
		__lowercase											=     True if "large" in model_name or "huge" in model_name else False
		__lowercase											=     True if "large" in model_name or "huge" in model_name else False
		__lowercase											=     True if "large" in model_name or "huge" in model_name else False

		if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
				if "fl3" in model_name:
						__lowercase											=     [3, 3, 3, 3]
						__lowercase											=     [5, 5, 5, 5]
				elif "fl4" in model_name:
						__lowercase											=     [4, 4, 4, 4]
						__lowercase											=     [3, 3, 3, 3]

		if "tiny" in model_name or "small" in model_name or "base" in model_name:
				__lowercase											=     [3, 3, 3, 3]
				if "lrf" in model_name:
						__lowercase											=     [3, 3, 3, 3]
				else:
						__lowercase											=     [2, 2, 2, 2]

		if "tiny" in model_name:
				__lowercase											=     96
		elif "small" in model_name:
				__lowercase											=     96
		elif "base" in model_name:
				__lowercase											=     128
		elif "large" in model_name:
				__lowercase											=     192
		elif "xlarge" in model_name:
				__lowercase											=     256
		elif "huge" in model_name:
				__lowercase											=     352

		# set label information
		__lowercase											=     "huggingface/label-files"
		if "large" in model_name or "huge" in model_name:
				__lowercase											=     "imagenet-22k-id2label.json"
		else:
				__lowercase											=     "imagenet-1k-id2label.json"

		__lowercase											=     json.load(open(hf_hub_download(_lowerCamelCase						,   _lowerCamelCase						,   repo_type='dataset'							)						,   'r'							)							)
		__lowercase											=     {int(_lowerCamelCase							): v for k, v in idalabel.items()}
		__lowercase											=     {v: k for k, v in idalabel.items()}

		__lowercase											=     FocalNetConfig(
		    embed_dim=_lowerCamelCase						,   depths=_lowerCamelCase						,   focal_levels=_lowerCamelCase						,   focal_windows=_lowerCamelCase						,   use_conv_embed=_lowerCamelCase						,   idalabel=_lowerCamelCase						,   labelaid=_lowerCamelCase						,   use_post_layernorm=_lowerCamelCase						,   use_layerscale=_lowerCamelCase						,   )

		return config



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:





		if "patch_embed.proj" in name:
				__lowercase											=     name.replace('patch_embed.proj'						,   'embeddings.patch_embeddings.projection'							)
		if "patch_embed.norm" in name:
				__lowercase											=     name.replace('patch_embed.norm'						,   'embeddings.norm'							)
		if "layers" in name:
				__lowercase											=     "encoder." + name
		if "encoder.layers" in name:
				__lowercase											=     name.replace('encoder.layers'						,   'encoder.stages'							)
		if "downsample.proj" in name:
				__lowercase											=     name.replace('downsample.proj'						,   'downsample.projection'							)
		if "blocks" in name:
				__lowercase											=     name.replace('blocks'						,   'layers'							)
		if "modulation.f.weight" in name or "modulation.f.bias" in name:
				__lowercase											=     name.replace('modulation.f'						,   'modulation.projection_in'							)
		if "modulation.h.weight" in name or "modulation.h.bias" in name:
				__lowercase											=     name.replace('modulation.h'						,   'modulation.projection_context'							)
		if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
				__lowercase											=     name.replace('modulation.proj'						,   'modulation.projection_out'							)

		if name == "norm.weight":
				__lowercase											=     "layernorm.weight"
		if name == "norm.bias":
				__lowercase											=     "layernorm.bias"

		if "head" in name:
				__lowercase											=     name.replace('head'						,   'classifier'							)
		else:
				__lowercase											=     "focalnet." + name

		return name



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case=False							)		->    Any:
		__lowercase											=     {
		    "focalnet-tiny": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth",
		    "focalnet-tiny-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth",
		    "focalnet-small": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth",
		    "focalnet-small-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth",
		    "focalnet-base": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth",
		    "focalnet-base-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth",
		    "focalnet-large-lrf-fl3": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth",
		    "focalnet-large-lrf-fl4": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth",
		    "focalnet-xlarge-lrf-fl3": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth",
		    "focalnet-xlarge-lrf-fl4": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth",
		}
		# fmt: on

		__lowercase											=     model_name_to_url[model_name]
		print('Checkpoint URL: '						,   _lowerCamelCase							)
		__lowercase											=     torch.hub.load_state_dict_from_url(_lowerCamelCase						,   map_location='cpu'							)["model"]

		# rename keys
		for key in state_dict.copy().keys():
				__lowercase											=     state_dict.pop(_lowerCamelCase							)
				__lowercase											=     val

		__lowercase											=     get_focalnet_config(_lowerCamelCase							)
		__lowercase											=     FocalNetForImageClassification(_lowerCamelCase							)
		model.eval()

		# load state dict
		model.load_state_dict(_lowerCamelCase							)

		# verify conversion
		__lowercase											=     "http://images.cocodataset.org/val2017/000000039769.jpg"

		__lowercase											=     BitImageProcessor(
		    do_resize=_lowerCamelCase						,   size={'shortest_edge': 256}						,   resample=PILImageResampling.BILINEAR						,   do_center_crop=_lowerCamelCase						,   crop_size=224						,   do_normalize=_lowerCamelCase						,   image_mean=_lowerCamelCase						,   image_std=_lowerCamelCase						,   )
		__lowercase											=     Image.open(requests.get(_lowerCamelCase						,   stream=_lowerCamelCase							).raw							)
		__lowercase											=     processor(images=_lowerCamelCase						,   return_tensors='pt'							)

		__lowercase											=     transforms.Compose(
		    [
		        transforms.Resize(256							),
		        transforms.CenterCrop(224							),
		        transforms.ToTensor(),
		        transforms.Normalize(mean=[0.485, 0.456, 0.406]						,   std=[0.229, 0.224, 0.225]							),
		    ]							)

		__lowercase											=     image_transforms(_lowerCamelCase							).unsqueeze(0							)

		# verify pixel_values
		assert torch.allclose(inputs.pixel_values						,   _lowerCamelCase						,   atol=1E-4							)

		__lowercase											=     model(**_lowerCamelCase							)

		__lowercase											=     outputs.logits.argmax(-1							).item()
		print('Predicted class:'						,   model.config.idalabel[predicted_class_idx]							)

		print('First values of logits:'						,   outputs.logits[0, :3]							)

		if model_name == "focalnet-tiny":
				__lowercase											=     torch.tensor([0.2166, -0.4368, 0.2191]							)
		elif model_name == "focalnet-tiny-lrf":
				__lowercase											=     torch.tensor([1.1669, 0.0125, -0.1695]							)
		elif model_name == "focalnet-small":
				__lowercase											=     torch.tensor([0.4917, -0.0430, 0.1341]							)
		elif model_name == "focalnet-small-lrf":
				__lowercase											=     torch.tensor([-0.2588, -0.5342, -0.2331]							)
		elif model_name == "focalnet-base":
				__lowercase											=     torch.tensor([-0.1655, -0.4090, -0.1730]							)
		elif model_name == "focalnet-base-lrf":
				__lowercase											=     torch.tensor([0.5306, -0.0483, -0.3928]							)
		assert torch.allclose(outputs.logits[0, :3]						,   _lowerCamelCase						,   atol=1E-4							)
		print('Looks ok!'							)

		if pytorch_dump_folder_path is not None:
				print(F"Saving model and processor of {model_name} to {pytorch_dump_folder_path}"							)
				model.save_pretrained(_lowerCamelCase							)
				processor.save_pretrained(_lowerCamelCase							)

		if push_to_hub:
				print(F"Pushing model and processor of {model_name} to the hub..."							)
				model.push_to_hub(F"{model_name}"							)
				processor.push_to_hub(F"{model_name}"							)


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      List[Any]			=   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--model_name''',
		    default='''focalnet-tiny''',
		    type=str,
		    help='''Name of the FocalNet model you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
		)
		parser.add_argument(
		    '''--push_to_hub''',
		    action='''store_true''',
		    help='''Whether to push the model and processor to the hub.''',
		)

		SCREAMING_SNAKE_CASE_   :      List[Any]			=   parser.parse_args()
		convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






import math




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     int)   ->       str:






							'''simple docstring'''





							__UpperCamelCase      :  Union[str, Any]       =   0
							__UpperCamelCase      :  int       =   0
							while num > 0:
														__UpperCamelCase      :  List[Any]       =   num % 8
														__UpperCamelCase      :  Tuple       =   octal + (remainder * math.floor(math.pow(10		,    _lowerCamelCase)))
														counter += 1
														__UpperCamelCase      :  Optional[Any]       =   math.floor(num / 8)  # basically /= 8 without remainder if any
														# This formatting removes trailing '.0' from `octal`.
							return F'0o{int(_lowerCamelCase)}'




def       _SCREAMING_SNAKE_CASE   (						)   ->       None:






							'''simple docstring'''





							print("\n2 in octal is:")
							print(decimal_to_octal(2))  # = 2
							print("\n8 in octal is:")
							print(decimal_to_octal(8))  # = 10
							print("\n65 in octal is:")
							print(decimal_to_octal(65))  # = 101
							print("\n216 in octal is:")
							print(decimal_to_octal(216))  # = 330
							print("\n512 in octal is:")
							print(decimal_to_octal(512))  # = 1000
							print("\n")


if __name__ == "__main__":
		main()